Unconstrained ML models can treat similar individuals differently due to small differences in input data values, causing local bias that is not detectable with standard DI testing methods that measure group fairness [1]}. To mitigate local bias when using ML, and to ensure standard bias or DI testing methods are most effective, pair such testing with constrained models.
